Chen Jiayu, Liu Jingyu, Calhoun Vince D
Dept. of Electrical Engineering, University of New Mexico, Albuquerque, NM.
The Mind Research Network, Albuquerque, NM.
IEEE Int Conf Bioinform Biomed Workshops. 2010 Dec;2010:827-828. doi: 10.1109/BIBMW.2010.5703928. Epub 2011 Jan 28.
Copy number variation (CNV) detection using SNP array data is challenging due to the low signal-to-noise ratio. In this study, we propose a principal component analysis (PCA) based correction to eliminate variance in CNV data induced by potential confounding factors. Simulations show a substantial improvement in CNV detection accuracy after correction. We also observe a significant improvement in data quality in real SNP array data after correction.
由于信噪比低，利用单核苷酸多态性（SNP）阵列数据进行拷贝数变异（CNV）检测具有挑战性。在本研究中，我们提出了一种基于主成分分析（PCA）的校正方法，以消除潜在混杂因素在CNV数据中引起的方差。模拟结果表明，校正后CNV检测准确性有显著提高。我们还观察到，校正后真实SNP阵列数据的数据质量有显著改善。
